Dataset collections - infrastructure glue.

Add an API and service layer for dataset collections.
Dataset collections - tool parameters - basics.

Add parameter, wrapper, etc... to explicitly utilize dataset collections from tools. These require actually modifing or creating new tools to take in "data_collection" parameters. Downstream changesets will provide a number of infrastructure enhancements to allow existing tools to "map" and "reduce" dataset collections with existing tools.
Dataset collections - tool parameters - enhanced rerun.

Rerunning tools in same history will work without this, but getting rerun to work on imported histories requires this hack (mirroring the same hack for HDAs in the same file).
Rework logic related to computing selected datasets.

Allow dataset ids to be used. This will make implementing multi-running of tools possible without expanding values out to hdas. check_params will still be called on each permutation so this shouldn't result in security problems.
Refactor Tool.handle_input result to return list of pairs instead of odict.

This added generality of allowing multiple outputs with the same output name will allow handle_input to execute multiple jobs in subsequent changesets.
Allow running tools on multiple datasets simulatenously (backend+API).

Each data tool parameter can be specified via a similar named parameter but with the suffix |__multirun__. This second parameter variant should be a list of datasets - one job will be created for each such dataset.
Allow running tools on multiple datasets simulatenously (UI).

Update tool form. This commit needs work to ensure this only affects the tool_form.mako.
Add tool meta parameter exception.
Dataset collections - "map" with existing tools (backend).

Allow replacing data parameter inputs with collections - this will cause the tool to produce multiple jobs for the submission - one for each combination of input parameters.
Dataset collections - "map" with existing tools (UI).

Add UI components needed for previous commit.
Dataset collections - implicit "map" creates collections.

When running normal tools with normal data inputs across dataset collections in parallel - a collection will be created for each output with a "structure" matching that of the input (for instance pairs will create pairs, lists will create lists with the same identifiers, etc...). A previous changeset added the ability to run the tool in parallel - this changeset extends that functionality to create analogous collections from these parallel runs.

For example, if one filters a pair of FASTQ files a pair is created out of the result. Likewise, if one filters a list of FASTQ files - a list collection with same cardinality is built from the results.

There is a lot left TODO here - for one a lot of this logic should be moved into the dataset_collection module. The matching needs to be exact right now - not a problem for pairs (every 'element' has name 'left' or 'right') but for lists with element names - these have to match exactly - but a 'list' like samp1_l, samp2_l, samp3_l should be able to match against samp1_r, samp2_r, samp3_r and create a new list samp1, samp2, and samp3. Even if there is no matching prefixes a new 'unlabeled' list should be able to be created.
Dataset collections - "reduce" with existing tools.

Allow users to select dataset collections in place of individual datasets for data tool parameters with multiple="true" enabled (if all elements of collection would be valid as input to this parameter).
Refactor workflow module to allow multiple kinds of inputs.
Dataset collections - workflows - allow data_collection params.

Extend workflow running, extraction, viewing to allow tools with data_collection parameters.

This is brutally ugly because "isinstance( input, DataToolParameter )" which was already not very Pythonic has been updated to be "isinstance( input, DataToolParameter ) or isinstance( input, DataCollectionToolParameter )". This should all be reworked with an "is_data_parameter" on base ToolParameter.
Dataset collections - workflows - allow implicit mapping steps.

While running a workflow, if a data tool parameter is encountered with a dataset collection as input - run N jobs and create a dataset collection for each of the outputs (similar functionality added for tools in previous changeset). These collections are then used in subsequent steps while connecting steps so the parallelization continues as does the creation of dataset collections.

Much and more remains to be done and tested - chief among these concerns I think is that in order to test this I needed to hand modify a workflow json file. The ability to use this feature should be encorporated into the editor and the interface/controller for extracting a workflow from a history.
Dataset collections - workflows - implicit "reduce" steps.
WIP: Dataset collections - workflows - implement extracting from history.

Display user created dataset collections as inputs. Find implicit mapping steps creating dataset collections and replace all individuals steps with mapping steps in UI and in generated workflow.

This commit works sortof - but needs significant cleanup - at very least it should be modified to work imported histories and the the code should be significantly refactored to clean things up. get_job_dict should return an object of some sort instead of a 4-tuple.
HACK: Modify contents type on index, need to remove at some point.

Need to find a way to make backbone do this.
Dataset collections - create lists and pairs via HDA multi-select.
Dataset collections - backbone model for history content.